Skip to main content

Vikash Gilja: Towards Clinically Viable Neural Prosthetic Systems.

Item Preview

There Is No Preview Available For This Item

This item does not appear to have any files that can be experienced on Archive.org.

Show all files

movies
Vikash Gilja: Towards Clinically Viable Neural Prosthetic Systems.


Publication date 2010-09-29
Talk by Vikash Gilja of Stanford University. Given on Sept. 29, 2010 to the Redwood Center for Theoretical Neuroscience at UC Berkeley.

Abstract.
By restoring the ability to move and communicate with the world, brain computer interfaces (BCIs) offer the potential to improve quality of life for people suffering from spinal cord injury, stroke, or neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS). BCIs attempt to translate measured neural signals into the user’s intentions and, subsequently, control a computer or actuator. Recently, compelling examples of intra-cortical BCIs have been demonstrated in tetraplegic patients. Although these studies provide a powerful proof-of-concept, clinical viability is impeded by limited performance and robustness over short (hours) and long (days) timescales.

We address performance and robustness over short time periods by approaching BCIs as a systems level design problem. We identify key components of the system and design a novel BCI from a feedback control perspective. In this perspective, the brain is the controller of a new plant, defined by the BCI, and the actions of this BCI are witnessed by the user. This simple perspective leads to design advances that result in significant qualitative and quantitative performance improvements. Through online closed loop experiments, we show that this BCI is capable of producing continuous endpoint movements that approach native limb performance and can operate continuously for hours. We also demonstrate how this system can be operated across days by a bootstrap procedure with the potential to eliminate an explicit recalibration step.

To examine the use of BCIs over longer timescales, we develop new electrophysiology tools that allow for continuous multi-day neural recording. Through application of this technology, we measure the signal acquisition stability (and instability) of the electrode array technology used in current BCI clinical trials. We also demonstrate how these systems can be used to study BCI decoding over longer time periods. In this demonstration, we present a simple methodology for switching BCI systems on and off at appropriate times.

The algorithms and methods demonstrated can be run with existing low power application specific integrated circuits (ASICs), with a defined path towards the development of a fully implantable neural interface system. We believe that these advances are a step towards clinical viability and, with careful user interface design, neural prosthetic systems can be translated into real world solutions.

comment
Reviews

There are no reviews yet. Be the first one to write a review.
In Collection
Community Video
Uploaded by
JeffT
on 9/30/2010
Views
127
SIMILAR ITEMS (based on metadata)
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 251
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 257
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 909
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 169
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 438
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 312
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 174
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 360
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 125
favorite 0
comment 0
Community Video
by Redwood Center for Theoretical Neuroscience
movies
eye 714
favorite 0
comment 0